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Ionic-liquid-based hollow-fiber liquid-phase microextraction method combined with hybrid artificial neural network-genetic algorithm for speciation and optimized determination of ferro and ferric in environmental water samples

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Abstract

A novel and environmentally friendly ionic-liquid-based hollow-fiber liquid-phase microextraction method combined with a hybrid artificial neural network (ANN)–genetic algorithm (GA) strategy was developed for ferro and ferric ions speciation as model analytes. Different parameters such as type and volume of extraction solvent, amounts of chelating agent, volume and pH of sample, ionic strength, stirring rate, and extraction time were investigated. Much more effective parameters were firstly examined based on one-variable-at-a-time design, and obtained results were used to construct an independent model for each parameter. The models were then applied to achieve the best and minimum numbers of candidate points as inputs for the ANN process. The maximum extraction efficiencies were achieved after 9 min using 22.0 μL of 1-hexyl-3-methylimidazolium hexafluorophosphate ([C6MIM][PF6]) as the acceptor phase and 10 mL of sample at pH = 7.0 containing 64.0 μg L−1 of benzohydroxamic acid (BHA) as the complexing agent, after the GA process. Once optimized, analytical performance of the method was studied in terms of linearity (1.3–316 μg L−1, R 2 = 0.999), accuracy (recovery = 90.1–92.3 %), and precision (relative standard deviation (RSD) <3.1). Finally, the method was successfully applied to speciate the iron species in the environmental and wastewater samples.

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Acknowledgments

The authors would like to thank Semnan University Research Council for financial support of this work. The authors also would like to especially thank Miss S. Sabzalian, Department of Chemistry, Semnan University, Semnan, Iran.

Conflict of interest

The authors declare no competing financial interest.

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Authors and Affiliations

  1. Iranian National Standards Organization, Khorasan-e-Razavi, Mashhad, Iran

    Iman Saeidi

  2. Department of Chemistry, Semnan University, Semnan, 35195-363, Iran

    Behruz Barfi & Alireza Asghari

  3. Department of Electrical and Computer Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran

    Abdorreza Alavi Gharahbagh

  4. Department of Biology, School of Science, Isfahan Payam-e Noor University, Isfahan, Iran

    Azadeh Barfi

  5. Department of Chemistry, University of Mazandaran, Babolsar, Iran

    Moazameh Peyrovi

  6. Department of Research and Development of Samen Pharmaceutical Company, Mashhad, Iran

    Maryam Afsharzadeh & Mostafa Hojatinasab

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  1. Iman Saeidi
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  2. Behruz Barfi
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  3. Alireza Asghari
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Correspondence to Behruz Barfi.

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Saeidi, I., Barfi, B., Asghari, A. et al. Ionic-liquid-based hollow-fiber liquid-phase microextraction method combined with hybrid artificial neural network-genetic algorithm for speciation and optimized determination of ferro and ferric in environmental water samples. Environ Monit Assess 187, 631 (2015). https://doi.org/10.1007/s10661-015-4860-4

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